Mapping the early language environment using all-day recordings and automated analysis


Purpose: This research provided a first-generation standardization of automated language environment estimates, validated these estimates against standard language assessments, and extended on previous research reporting language behavior differences across socioeconomic groups. Method: Typically developing children between 2 to 48 months of age completed monthly, daylong recordings in their natural language environments over a span of approximately 6–38 months. The resulting data set contained 3,213 12-hr recordings automatically analyzed by using the Language Environment Analysis (LENA) System to generate estimates of (a) the number of adult words in the child’s environment, (b) the amount of caregiver–child interaction, and (c) the frequency of child vocal output. Results: Child vocalization frequency and turn-taking increased with age, whereas adult word counts were age independent after early infancy. Child vocalization and conversational turn estimates predicted 7%– 16% of the variance observed in child language assessment scores. Lower socioeconomic status (SES) children produced fewer vocalizations, engaged in fewer adult–child interactions, and were exposed to fewer daily adult words compared with their higher socioeconomic status peers, but within-group variability was high. Conclusions: The results offer new insight into the landscape of the early language environment, with clinical implications for identification of children at-risk for impoverished language environments.

Publication Title

American Journal of Speech-Language Pathology